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Progressive Seismic Data Mining for Reservoir Characterization: Lake Theriot 3-D Survey, Terrebonne Parish, Louisiana [Abstract]
Seismic interpreters are required to work with larger and larger seismic volumes as the amount of seismic data we acquire and process continues to increase. Rapid advances in seismic attribute methods further increase our data-set sizes by providing many coincident seismic attribute volumes for each data set.
These exponential increases in available data represent huge data management and data interpretation challenges to our industry. There are clear similarities between the seismic exploration industry and the Internet in terms of the volume of information that is available for analysis, and therefore it makes sense to deploy data mining tools and methodologies developed for other industries to address the needs of the oil and gas exploration business. Here we employ some aspects of the data mining workflow to enrich and discover knowledge about possibly productive regions within a 3-D seismic data volume from South Louisiana.
Seismic data mining is applied to multiple seismic attribute volumes calculated from a 3-D dataset acquired for the Lake Theriot area, Terrebonne Parish, South Louisiana. Various instantaneous and geometric seismic attributes are used during data reduction for the rapid delineation of a possibly prospective, faulted subsurface channel system and the seismic properties of its sediment fill. Additionally, selected seismic attributes of the propagated wave field can be recombined mathematically to produce an algorithm that encapsulates geophysically descriptive aspects of subsurface seismic facies. Seismic attributes including variance of angle, time variance of instantaneous frequency, and variance of similarity, combine to form the "Shale Indicator". This is a "hybrid" seismic attribute that integrates certain depositional characteristics of shales, such as lateral continuity, thin-bed layering, and parallelism of bedding, in an effort to seismically differentiate "seismic shales" from "seismic non-shales."
Additionally, application of neural network technology generates a single attribute volume of the multi-attribute response illuminating discrete seismic facies. The results of this study demonstrate the value of applying data mining techniques to seismic data volumes to rapidly establish zone prospectivity, thereby mitigating future drilling risk.
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